Performing Big Data Engineering on Microsoft Cloud Services - Moc On Demand (MS-20776)
About this course:
What is big data? Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves. This course describes how to process Big Data using Azure tools and services including Azure Stream Analytics, Azure Data Lake, Azure SQL Data Warehouse and Azure Data Factory. The course also explains how to include custom functions, and integrate Python and R.
The average salary for Big Data Engineer is $90,286 per year.
Upcoming start dates
- Self-paced Online
Who should attend?
This course is intended for:
- Data engineers (IT professionals, developers, and information workers)
- A good understanding of Azure data services.
- A basic knowledge of the Microsoft Windows operating system and its core functionality.
- A good knowledge of relational databases.
After completing this course, students will be able to:
- Describe common architectures for processing big data using Azure tools and services.
- Describe how to use Azure Stream Analytics to design and implement stream processing over large-scale data.
- Describe how to include custom functions and incorporate machine learning activities into an Azure Stream Analytics job.
- Describe how to use Azure Data Lake Store as a large-scale repository of data files.
- Describe how to use Azure Data Lake Analytics to examine and process data held in Azure Data Lake Store.
- Describe how to create and deploy custom functions and operations, integrate with Python and R, and protect and optimize jobs.
- Describe how to use Azure SQL Data Warehouse to create a repository that can support large-scale analytical processing over data at rest.
- Describe how to use Azure SQL Data Warehouse to perform analytical processing, how to maintain performance, and how to protect the data.
- Describe how to use Azure Data Factory to import, transform, and transfer data between repositories and services.
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